Journal Article

Support vector regression model with variant tolerance

He, Xiaoxia, Wei, Jiangyue

2023

Overview

Most works on Support Vector Regression (SVR) focus on kernel or loss functions, with the corresponding support vectors obtained using a fixed-radius ε -tube, affording good predictive performance on datasets. However, the fixed radius limitation prevents the adaptive selection of support vectors according to the data distribution characteristics, compromising the performance of the SVR-based methods. Therefore, this study proposes an “Alterable ε i -Support Vector Regression” ( A ε i -SVR) model by applying a novel ε , named “Alterable ε i ,” to the SVR model. Based on the data point sparsity at each location, the model solves the different ε i at the corresponding position, and thus zoom-in or zoom-out the ε -tube by changing its radius. Such a variable ε -tube strategy diminishes noise and outliers in the dataset, enhancing the prediction performance of the A ε i -SVR model. Therefore, we suggest a novel non-deterministic algorithm to iteratively solve the complex problem of optimizing ε i associated with every location. Extensive experimental results demonstrate that our approach can improve the accuracy and stability on simulated and real data compared with the baseline methods.

• Publisher: SAGE Publications,Sage Publications Ltd,SAGE Publishing
• Subject: Algorithms / Data points / Datasets / Outliers (statistics) / Performance prediction / Regression models / Support vector machines